Using artificial neural networks to extract the 21-cm global signal from the EDGES data

Abstract

The redshifted 21-cm signal of neutral hydrogen is a promising probe into the period of evolution of our Universe when the first stars were formed (Cosmic Dawn), to the period where the entire Universe changed its state from being completely neutral to completely ionized (Reionization). The most striking feature of this line of neutral hydrogen is that it can be observed across an entire frequency range as a sky-averaged continuous signature, or its fluctuations can be measured using an interferometer. However, the 21-cm signal is very faint and is dominated by a much brighter Galactic and extragalactic foregrounds, making it an observational challenge. We have used different physical models to simulate various realizations of the 21-cm global signals, including an excess radio background to match the amplitude of the Experiment to Detect the Global EoR Signature (EDGES) 21-cm signal. First, we have used an artificial neural network (ANN) to extract the astrophysical parameters from these simulated data sets. Then, mock observations were generated by adding a physically motivated foreground model and an ANN was used to extract the astrophysical parameters from such data. The R2 score of our predictions from the mock observations is in the range of 0.65-0.89. We have used this ANN to predict the signal parameters giving the EDGES data as the input. We find that the reconstructed signal closely mimics the amplitude of the reported detection. The recovered parameters can be used to infer the physical state of the gas at high redshifts. © 2021 The Author(s) Published by Oxford University Press on behalf of Royal Astronomical Society.